code: def show_plot(X, y, n_neighbors=10, h=0.2):
      # Create color maps
      cmap_light = ListedColormap(
          [
              "#FFAAAA",
              "#AAFFAA",
              "#AAAAFF",
              "#FFAAAA",
              "#AAFFAA",
              "#AAAAFF",
              "#FFAAAA",
              "#AAFFAA",
              "#AAAAFF",
              "#AAAAFF",
          ]
      )
      cmap_bold = ListedColormap(
          [
              "#FF0000",
              "#00FF00",
              "#0000FF",
              "#FF0000",
              "#FF0000",
              "#FF0000",
              "#FF0000",
              "#FF0000",
              "#FF0000",
              "#FF0000",
          ]
      )
  
      for weights in ["uniform", "distance"]:
          # we create an instance of Neighbours Classifier and fit the data.
          clf = neighbors.KNeighborsClassifier(
              n_neighbors, weights=weights
          )
          clf.fit(X, y)
          clf.n_neighbors = n_neighbors
  
          # Plot the decision boundary. For that, we will assign a color to each
          # point in the mesh [x_min, x_max]x[y_min, y_max].
          x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
          y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
          xx, yy = np.meshgrid(
              np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)
          )
          Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  
          # Put the result into a color plot
          Z = Z.reshape(xx.shape)
          plt.figure()
          plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
  
          # Plot also the training points
          plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
          plt.xlim(xx.min(), xx.max())
          plt.ylim(yy.min(), yy.max())
          plt.title(
              "3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights)
          )
  
      plt.show()
  
file_path: yellowbrick/examples/balavenkatesan/testing.py
imports: import pandas
  import csv
  from sklearn import neighbors
  from sklearn import datasets
  import numpy
  import yellowbrick
  from yellowbrick.neighbors import KnnDecisionBoundariesVisualizer
  import matplotlib.pyplot
  from matplotlib.colors import ListedColormap
label_desc: Use image node_id 2 for calling a global function with example usage: show_plot(X, y, n_neighbors, h) without return types